When Jason Nichols joined GE Global Research in 2011, soon after completing postdoctoral work in organic chemistry at the University of California, Berkeley, he anticipated a long career in chemical research. But after four years creating materials and systems to treat industrial wastewater, Nichols moved to the company’s machine-learning lab. This year he began working with augmented reality. Part chemist, part data scientist, Nichols is now exactly the type of hybrid employee crucial to the future of a company working to inject artificial intelligence into its machines and industrial processes.

Fifteen years ago, GE’s machine operators and technicians monitored its aircraft engines, locomotives, and gas turbines by listening to their clanks and whirs and checking their gauges. Today, the company uses AI to do the equivalent, even predicting failures in advance (see “50 Smartest Companies 2017.”). By marshaling this technology, GE hopes to become one of the world’s top software providers by 2020, a quest that amped up in 2011 with a $1 billion initiative to collect and analyze sensor data from machines. Creating smarter models via AI is the next step in the company’s strategy—one that it hopes will give it an advantage over longtime rivals like Siemens and software giants, such as IBM, that are now expanding into industrial analytics.

Of course, integrating artificial intelligence into an organization founded in 1892 is a difficult task. It starts with training the technical brains behind the company, which employs 300,000 people across all its businesses worldwide. GE Global Research, where Jason Nichols works, is setting up online programs that teach machine learning and symposia where scientists can explore new roles. So far, nearly 400 employees from across the company have completed GE’s certification program for data analytics, and about 50 scientists have moved into digital analytics jobs of the kind Nichols has taken on.